EP3255592A1 - System zur messung der performance von musikern auf digitalen plattformen - Google Patents

System zur messung der performance von musikern auf digitalen plattformen Download PDF

Info

Publication number
EP3255592A1
EP3255592A1 EP16173560.0A EP16173560A EP3255592A1 EP 3255592 A1 EP3255592 A1 EP 3255592A1 EP 16173560 A EP16173560 A EP 16173560A EP 3255592 A1 EP3255592 A1 EP 3255592A1
Authority
EP
European Patent Office
Prior art keywords
artist
count data
online platform
fan
computer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP16173560.0A
Other languages
English (en)
French (fr)
Inventor
Lukas Cudrigh
Paul Tomochko
Scott Rutherford
Navine Karim
Marcus Heisser
Cam Setzer
Geoff Chandler
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Red Bull Media House North America
Original Assignee
Red Bull Media House North America
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Red Bull Media House North America filed Critical Red Bull Media House North America
Priority to EP16173560.0A priority Critical patent/EP3255592A1/de
Publication of EP3255592A1 publication Critical patent/EP3255592A1/de
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data

Definitions

  • Digital download stores including iTunes and Amazon Music, offer both singles and full-length album sales, which compete directly with the sale of physical units, like CDs.
  • Digital music streaming products offer several different models of listening including free, ad-supported non-interactive experiences, to subscription-based services that enable users to stream an unlimited amount of music, on-demand, through Internet connected devices. Examples of these types of digital media platforms include Spotify, Pandora, Apple Music, and Google Play.
  • music fans increasingly adopt social media platforms, such as Facebook, Twitter, Snapchat and Instagram to follow artists and discover new music. Artists utilize these social networks to build a fan base and promote media content, such as video clips, audio files, pictures, messages, tweets, etc.
  • Many platforms, like SoundCloud and YouTube enable both media and social interactions.
  • the present invention is a system for measuring, ranking and classifying music artists based on their performance across digital media platforms and social networks.
  • the objective of the invention is to apply a computer-implemented method to collect media consumption data (PLAYS) from digital music streaming and download services and fan growth data (FANS) from social media networks to calculate a numerical score that indicates how a music artist is performing and to compare their performance against a group of their peers.
  • PLAYS media consumption data
  • FANS fan growth data
  • the system automatically communicates with the one or more digital media platforms and/or social networks to obtain the media consumption data and/or fan growth data.
  • the centralized system can communicate with the one or more digital media platforms and/or social networks at set time intervals to receive the media consumption data and/or fan growth data and/or update the media consumption data and/or fan growth data.
  • the system can communicate with the one or more digital media platforms per minute, hour, day, etc. to receive and/or update the media consumption data and/or fan growth data.
  • the system can continuously communicate with the one or more digital media platforms to receive and/or update the media consumption data and/or fan growth data.
  • the system applies a series of advanced equations to calculate a numerical score, named the Artist Digital Footprint (ADF).
  • ADF the Artist Digital Footprint
  • the system utilizes the RAW FAN COUNT (RFC) and RAW PLAY COUNT (RPC) collected from the digital platforms to determine the size of the audience the artists has developed.
  • RFC RAW FAN COUNT
  • RPC RAW PLAY COUNT
  • the ADF takes into consideration the value of RFC compared to RPC. This is achieved by applying a normalization factor, which is the ratio of the lognormal fit of RFC and RPC spanning a representative set of music artists.
  • the ADF is used to evaluate the size of an artist's audience and performance at a certain point of time. For a music business, the ADF provides a standardized measure to rank and compare the performance of a portfolio of music artists within a predetermined group of artists. This includes generating charts to determine the relative popularity of their artists.
  • the same RFC and RPC inputs are applied to calculate the Artist Growth (AG) for a single artist or a group of artists.
  • the AG for single music artist is produced, by calculating the percentage change of the weighted scores of RFC and RPC over a specific period of time.
  • the percentage score is determined by calculating the weighted average percentage change for all the artists in a group.
  • artist growth scores below a certain threshold will have to be eliminated. This threshold is defined through minimum RFC and RPC counts required by artist to be included in the AG for a group of artists.
  • Music artists consider the AG as the primary measure of performance of their activities including record releases, gigs and promotional activities.
  • a music business applies the AG to measure and track the performance of their artists and their artist portfolios across genres, geographies or the portfolio as a whole.
  • Another calculation used in the invention is Artist Growth Score (AGS), which is a score that indicates an artist whose RFC and RPC are growing faster than others. This involves calculating a score for an individual artist's growth over multiple timeframes and then comparing the score with a group of artists.
  • AGS signals that an artist is possibly "breaking” or “trending” and their popularity is rapidly increasing.
  • the method outputs the AGS and/or ADF based on the artist, artist portfolios across genres, geographies or the portfolio as a whole. For instance, the system can rank an artist within the group of artists using the genre(s), geography(ies), portfolio of the artist, etc. The system can then output a ranking for the artist within the group of artists on a display. In some examples, the output includes a list of artists that includes the artist and one or more of the artists from the group of artists. The system can create the list of artists using the ranking for the artist and rankings for the one or more artists. In some examples, when causing display of the list of artists, the system can cause the output to display a portion of the list that includes the artist.
  • the system can output the list of artists based on the capabilities (e.g., screen size) of the display.
  • a display that outputs the list of artists, AGS, and/or the ADF can include a limited screen size that cannot display the entire list of artists.
  • the system can output a sublist of breaking or trending artists by displaying the portion of the list that includes the artist based on the AGS and/or ADF for the artist. By outputting the portion of the list, a user does not have to scroll through the list of artists in order to determine where the artist is ranked within the list of artists.
  • FIG. 1 represents the centralized online platform 110 that communicates with a plurality of third party platforms 115 to aggregate RAW FAN COUNT (RFC) and RAW PLAY COUNT (RPC) data inputs that are used to calculate ADF, AG, and AGS.
  • RAW FAN COUNT RAW FAN COUNT
  • RPC RAW PLAY COUNT
  • the centralized platform 110 automatically communicates with the one or more online platforms 115 to obtain the fan RFC and RPC data inputs that are used to calculate ADF, AG, and AGS.
  • the centralized platform 110 can communicate with the one or more online platforms 115 at set time intervals to receive the data and/or update the data. For instance, the centralized platform 110 can communicate with the one or more online platforms 115 every minute, hour, day, etc. to receive and/or update the data. Additionally or alternatively, the centralized platform 110 can continuously communicate with the one or more online platforms 115 to receive and/or update the data of or for an artist 105.
  • the centralized platform 110 does not require input to obtain the data.
  • RAW FAN COUNT is defined as the total amount of followers (Twitter, Instagram, SoundCloud), likes (Facebook), subscribers (YouTube) that an artist 105 has acquired on social networks 115.
  • the RFC represents the artist's total addressable fan base on social networks 115.
  • the centralized online platform 110 obtains the raw count/number of followers/likes/subs from all connected social accounts via open APIs with the social networks 115.
  • RAW PLAY COUNT is defined as the total amount of music track streams (Spotify, Pandora, SoundCloud), music video streams (YouTube) and music track downloads (iTunes, GooglePlay).
  • the RPC represents the total amount of PLAYS that happen on the digital media platforms. Artist music tracks and music videos are distributed and tracked on digital music platforms in 3 ways:
  • This invention produces 3 numerical indicators to determine how well an artist is performing on digital platforms:
  • AGS is a score that indicates artists whose RFC and RPC are growing faster than others and could possibly be "breaking". This involves calculating a score for an individual artist's 105 growth over several different timeframes (short, long, etc.) and then comparing the score with a group of other artists. AGS could be used to rank artist 105 in their program, with the top rank having the fastest growth rate.
  • the Artist Digital Footprint accounts for the relationship between individual artist's FANS (the number of people an artist is connected with across all social networks) and PLAYS (the number of plays, streams, and downloads across all digital media platforms). This concept is indicated by FIG. 2 .
  • a Normalization Factor is calculated as a ratio of the means of the lognormal fits of the RFC and RPC for all artists of a given sample. Examples of lognormal distributions of FANS and lognormal distributions of PLAYS are shown in FIGs. 3.a and 3.b .
  • the Normalization Factor Prior to executing the full equation for the ADF, the Normalization Factor may be calculated as follows:
  • the number is transformed into a contextual score that can be used to gauge the amount of normalized RFC and RPC an artist 105 has compared to another artist.
  • the contextual score for the ADF could fit inside a scale of 1-10, where the lowest number represents an artist that is just starting their career with a very small or local fan base, and conversely, the highest number represents an artist 105 that has a large social following and has developed a global audience.
  • This scale can also be used to help an aspiring artist understand the requirements to become a mainstream artist.
  • the ADF can inform an aspiring artist how their current RFC and RPC compare to an artist who has a national audience.
  • the ADF Score will provide a contextual ranking of that artist's (digital) fan base/audience within a relevant group of other artists (e.g., select artist portfolio, overall industry, etc.) and can be used to inform and encourage actions that drive further ADF growth.
  • the ADF can be used to rank or categorize artists 105 in a way that accounts for digitization of the modern music experience.
  • Traditional ranking systems typically use downloads or physical album sales as their exclusive indicator of performance.
  • the ADF accounts for additional quantifiable variables, such as RFC on social media networks and RPC on digital media platforms 115.
  • artist rankings and categorization by ADF can be used to support artist portfolio management efforts and decisions including artist evaluation and selection, levels of investment into an artist and risk management (i.e., which artists to drop).
  • the Artist Growth (AG) calculation provides insight into the change in the RFC and RPC of an individual artist, or a group of artists, over a specified period of time. When applied to a single artist 105 it collapses to the simple combined average of the percentage change of their RFC and RPC; applied to a group the result is the weighted percentage change.
  • AG is a key metric for tracking the progression and trending of artists in the music industry. By examining changes in RFC and RPC data for an individual artist 105, or a group of artists, over a specified period of time, AG provides significant insight into the performance and advancement of a music career.
  • FIG. 5.a shows a concept of calculating the artist growth for an individual artist 105.
  • AG for an individual artist 105 is found by calculating the average of the weighted percentage change of their RFC and RPC. Weights in this calculation ( ⁇ (RFC i ) and ⁇ (RPC i )) are used to ensure that when calculating AG, the actual size of an artist's presence on each social media network 115 and digital media service is included in the equation. For example, over the course of a month, if an artist 105 increases their Facebook fan base from 50,000 to 100,000 followers and increases their Twitter fan base from 50 to 100 followers, the % change on each platform is equivalent. However, the increase in the actual number of followers over that month on Facebook is a much greater achievement. In order to accommodate for this disparity, within the Artist Growth calculation for each artist, changes on each network and service are weighted in proportion to their size relative to each other.
  • the weight of a given network, ⁇ ( RFC i ) is equal to the ratio of the RFC on the network at time T2 to the sum of all RFC on all of the artist's social networks at time T2:
  • ⁇ RFC i RFC i T 2
  • ⁇ i 1 n RFC i T 2
  • the change in RFC on a given network, ⁇ RFC i between time T1 and T2 is:
  • ⁇ RFC i RFC i T 2 ⁇ RFC i T 1 RFC i T 1 where RFC i is equal to the audience for an artist on a social network as defined above in the ADF Calculation.
  • ⁇ RPC i RPC i T 2 ⁇ RPC i T 1 RPC i T 1
  • RPC i is equal to the consumption of artist content on a digital media service as defined above in the ADF Calculation.
  • FIG. 5.b shows a concept of calculating the artist growth for a group of artists. Growth for a group of artists is the weighted average percentage change over a given time period of all artists in the target group.
  • some basic cuts are applied to the inbound data. The first of these cuts is the RFC Cut Threshold, a configurable lower bound requirement of FANS (ex. 100) across all social media networks.
  • the Normalization Factor (as described above) is used to determine the equivalent lower bound requirement for an artist's PLAYS across all digital media services, also referred to as the RPC Cut Threshold.
  • RPC Cut Threshold ⁇ RFC ⁇ RPC ⁇ RFC Cut Threshold
  • the AG calculation will provide a near-real time view of their pace of growth over time. Also, AG could be used to measure the performance of any promotional or marketing campaigns targeted at increasing exposure for the artist. This can include promoting the release of a new track, or an upcoming gig.
  • AG could be used to measure the performance and effectiveness of program focused on developing audiences for emerging music artists 105.
  • the equation is used to calculate the individual AG for each artist 105 in the program.
  • the average AG for all the artists 105 in the program is calculated to measure the effectiveness of the program as a whole.
  • An example of this could include setting a target of 3x ADF growth for all the artists 105 in the program, over a period of one year. If the average AG for all artists in the program at the end of the year is 3 times greater than at the start of the year, we could determine that the program is successful in driving both RFC and RPC for the artist, thus growing their overall addressable digital audience.
  • the Artist Growth Score is a numerical score that indicates when an artist 105 is growing their RFC and RPC faster than other artists in a group or cluster.
  • the AGS identifies when an artist is "breaking" or “trending", which can be defined as when an artist experiences explosive growth in RFC and RPC when compared to other artists in the group. Identifying artists 105 who are about to "break” involves taking a group of artists, measuring each artist's rate of growth over several different timeframes (short, long, etc.) and then comparing that with the rest of the group. The concept of the artist growth score is further illustrated in FIG. 6 .
  • the formula also has three tuning parameters ( ⁇ , ⁇ , ⁇ ) used to apply weights to each time period.
  • the tuning parameters allow for greater importance to be placed on one time period over another.
  • Each parameter can be adjusted to observe the sensitivity of the results, so long as they add up to 1 or 100%.
  • the final input into the equation is the Repress weight, which serves two purposes:
  • the R variable can be adjusted to observe the sensitivity of the results when evaluating a group of artists 105.
  • the AGS calculation will identify which artists are trending either upwards or downwards in terms of online fan growth. It can also be used to identify a "breaking" or “trending” artist at an early stage (not possible using manual methods).
  • One application is to use AGS to rank artists on a daily basis. Artists 105 whose AGS increases will be flagged as "trending artists” which represents that the artist RFC and RPC are growing at a faster rate than other artists in the pool.
  • FIG. 7 is a block diagram depicting an example computing device 600 of centralized platform 110 from FIG. 1 .
  • Device 600 can belong to a variety of categories or classes of devices such as traditional server-type devices, desktop computer-type devices, mobile devices, special purpose-type devices, embedded-type devices, and/or wearable-type devices. Thus, although illustrated as desktop computers, device 600 can include a diverse variety of device types and are not limited to a particular type of device.
  • Device 600 can represent, but are not limited to, desktop computers, server computers, web-server computers, personal computers, mobile computers, laptop computers, tablet computers, thin clients, terminals, personal data assistants (PDAs), work stations, integrated components for inclusion in a computing device, or any other sort of computing device.
  • PDAs personal data assistants
  • Device 600 can include any type of computing device having one or more processing unit(s) 602 operably connected to computer-readable media 604 such as via a bus 606, which in some instances can include one or more of a system bus, a data bus, an address bus, a PCI bus, a Mini-PCI bus, and any variety of local, peripheral, and/or independent buses.
  • Executable instructions stored on computer-readable media 604 can include, for example, an operating system 608, a digital footprint tool 610, and other modules, programs, or applications that are loadable and executable by processing units(s) 602.
  • the functionality described herein can be performed, at least in part, by one or more hardware logic components such as accelerators.
  • an accelerator can represent a hybrid device, such as one from ZYLEX or ALTERA that includes a CPU course embedded in an FPGA fabric.
  • computer-readable media 604 also includes a data store 612.
  • data store 612 includes data storage such as a database, data warehouse, a relational database, or other type of structured or unstructured data storage.
  • Data store 612 can store data for the operations of processes, applications, components, and/or modules stored in computer-readable media 604 and/or executed by processing unit(s) 602. Alternately, some or all of the above-referenced data can be stored on separate memories 614 on board one or more processing unit(s) 602 such as a memory on board a CPU-type processor, a GPU-type processor, an FPGA-type accelerator, a DSP-type accelerator, and/or another accelerator.
  • Device 600 can further include one or more input/output (I/O) interfaces 616 to allow device 600 to communicate with input/output devices such as user input devices including peripheral input devices (e.g., a keyboard, a mouse, a pen, a game controller, a voice input device, a touch input device, a gestural input device, and the like) and/or output devices including peripheral output devices (e.g., a display, a printer, audio speakers, a haptic output, and the like).
  • I/O input/output
  • peripheral input devices e.g., a keyboard, a mouse, a pen, a game controller, a voice input device, a touch input device, a gestural input device, and the like
  • peripheral output devices e.g., a display, a printer, audio speakers, a haptic output, and the like.
  • Device 600 can also include one or more network interfaces 618 to enable communications between device 600 and other networked devices, such as third party platforms 115 from FIG. 1 .
  • network interface(s) 618 can include one or more network interface controllers (NICs) or other types of transceiver devices to send and receive communications over a network.
  • NICs network interface controllers
  • digital footprint tool 610 can include one or more modules and/or APIs, which are illustrated as blocks 620, 622, 624, 626, and 628, although this is just an example, and the number can vary higher or lower. Functionality described associated with blocks 620, 622, 624, 626, 628 can be combined to be performed by a fewer number of modules and/or APIs or it can be split and performed by a larger number of modules and/or APIs.
  • block 620 can represent a music/content distribution module with logic to program processing unit 602 of device 600 for uploading and distributing artist music and content across a globally integrated media distribution network (e.g., the third party platforms 115 from FIG. 1 ).
  • Block 622 can represent a music/content promotion module with logic to program processing unit 602 of device 600 for providing artists with digital marketing tools for executing online promotional campaigns for the artist's music and related content.
  • Block 624 can represent a live-show promotion module with logic to program processing unit 602 of device 600 for providing artists with digital marketing tools for executing online promotional campaigns for an artist's live shows, tours, and big moments (e.g., music festival or TV appearance).
  • Block 626 can represent a fan engagement module with logic to program processing unit 602 of device 600 for providing an artist with a tool that helps the artist identify and better engage the artist's fan base.
  • fan engagement module 626 can obtain information about top fans (determined by music and content engagement, live show attendance, unsolicited online sharing and promotion of artist content, etc.), where the information can be used to inform promotional campaigns (music/content, live shows), plan live shows/tours and propel an ongoing conversation with fans within local and online music scenes.
  • Direct and authentic artist-to-fan "rewards" e.g., upgraded access to content, live shows
  • delivered to top fans drives social currency and motivates fans to bolster further artist support.
  • Block 628 can represent a profile management module with logic to program processing unit 602 of device 600 for creating and updating public artist profiles, band member administration and overall name and likeness, and extensions to any relevant music platforms, social channels, etc.
  • Computer-readable media 604 can include storage media and/or signal media. In some examples, computer-readable media 604 is specifically described as non-volatile memory. Otherwise, storage media includes tangible storage units such as volatile memory, nonvolatile memory, and/or other persistent and/or auxiliary storage media, removable and non-removable storage media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data.
  • Storage media includes tangible or physical forms of media included in a device or hardware component that is part of a device or external to a device, including but not limited to random-access memory (RAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), phase change memory (PRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, compact disc read-only memory (CD-ROM), digital versatile disks (DVDs), optical cards or other optical storage media, magnetic cassettes, magnetic tape, magnetic disk storage, magnetic cards or other magnetic storage devices or media, solid-state memory devices, storage arrays, network attached storage, storage area networks, hosted computer storage or memories, storage, devices, and/or storage media that can be used to store and maintain information for access by a corresponding computing device.
  • RAM random-access memory
  • SRAM static random-access memory
  • DRAM dynamic random-access memory
  • PRAM phase change memory
  • ROM read-only memory
  • signal media can embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanism.
  • storage media does not include signal media.
  • RFC Mean of the lognormal distribution of the total number of fans for active artists in the music industry
  • RPC Mean of the lognormal distribution of the total number of media content plays, streams and downloads for active artists in the music industry

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Data Mining & Analysis (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
EP16173560.0A 2016-06-08 2016-06-08 System zur messung der performance von musikern auf digitalen plattformen Withdrawn EP3255592A1 (de)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP16173560.0A EP3255592A1 (de) 2016-06-08 2016-06-08 System zur messung der performance von musikern auf digitalen plattformen

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP16173560.0A EP3255592A1 (de) 2016-06-08 2016-06-08 System zur messung der performance von musikern auf digitalen plattformen

Publications (1)

Publication Number Publication Date
EP3255592A1 true EP3255592A1 (de) 2017-12-13

Family

ID=56116333

Family Applications (1)

Application Number Title Priority Date Filing Date
EP16173560.0A Withdrawn EP3255592A1 (de) 2016-06-08 2016-06-08 System zur messung der performance von musikern auf digitalen plattformen

Country Status (1)

Country Link
EP (1) EP3255592A1 (de)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11301878B2 (en) * 2017-06-01 2022-04-12 Databook Labs Inc. Peer-group based business information system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006118405A1 (en) * 2005-05-02 2006-11-09 Silentmusicband Corp. Internet music composition application with pattern-combination method
US20130238444A1 (en) * 2011-07-12 2013-09-12 Sam Munaco System and Method For Promotion and Networking of at Least Artists, Performers, Entertainers, Musicians, and Venues
WO2014145974A1 (en) * 2013-03-15 2014-09-18 Isquith Jack System and method for scoring and ranking digital content based on activity of network users

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006118405A1 (en) * 2005-05-02 2006-11-09 Silentmusicband Corp. Internet music composition application with pattern-combination method
US20130238444A1 (en) * 2011-07-12 2013-09-12 Sam Munaco System and Method For Promotion and Networking of at Least Artists, Performers, Entertainers, Musicians, and Venues
WO2014145974A1 (en) * 2013-03-15 2014-09-18 Isquith Jack System and method for scoring and ranking digital content based on activity of network users

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11301878B2 (en) * 2017-06-01 2022-04-12 Databook Labs Inc. Peer-group based business information system

Similar Documents

Publication Publication Date Title
Malthouse et al. Managing customer relationships in the social media era: Introducing the social CRM house
Libai et al. Decomposing the value of word-of-mouth seeding programs: Acceleration versus expansion
Van de Vrande et al. How prior corporate venture capital investments shape technological alliances: A real options approach
Moe et al. Modeling hedonic portfolio products: A joint segmentation analysis of music compact disc sales
CN108537567B (zh) 一种目标用户群体的确定方法和装置
Appio et al. Unveiling the intellectual origins of social media-based innovation: insights from a bibliometric approach
Ballings et al. Social media optimization: Identifying an optimal strategy for increasing network size on Facebook
US9026594B2 (en) Method and system for determining user impact on their content pools within an online social network
US20160063523A1 (en) Feedback instrument management systems and methods
Trusov et al. Improving prelaunch diffusion forecasts: Using synthetic networks as simulated priors
CN106446078A (zh) 一种信息的推荐方法和推荐装置
Medvedev et al. Modelling structure and predicting dynamics of discussion threads in online boards
JP5910316B2 (ja) 情報処理装置、情報処理方法、および、プログラム
Simmie et al. Ranking twitter influence by combining network centrality and influence observables in an evolutionary model
Chan et al. Review of evaluation metrics used in digital and traditional tobacco control campaigns
US20140122504A1 (en) Systems and Methods for Collection and Automatic Analysis of Opinions on Various Types of Media
CN106708871B (zh) 一种社交业务特征用户的识别方法和装置
Stodder Customer analytics in the age of social media
Bolduc et al. Examining the impact of social identification with music on music streaming behavior
Collis What is entertainment? The value of industry definitions
Saldivar et al. Idea Management Communities in the Wild: An Exploratory Study of 166 Online Communities
Choicharoon et al. Hit or miss: A decision support system framework for signing new musical talent
EP3255592A1 (de) System zur messung der performance von musikern auf digitalen plattformen
CN110209944B (zh) 一种股票分析师推荐方法、装置、计算机设备和存储介质
US20170357719A1 (en) System for measuring the performance of music artists on digital platforms

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20180614